Continual learning is not restricted to computer vision. Asghar et al. [1]} proposed to solve the task of Natural Language Inference (NLI) when faced to an incremental stream of task datasets. Each dataset is a text corpus belong to particular domain [2]}; e.g. fiction, letters, face-to-face, etc. This application belongs to the lifelong learning group where the concepts are kept fixed but the input distribution evolves through time. More recently Lovon et al. [3]} explored the domain of information retrieval when faced to a stream of multiple text sources. While still minor compared to computer vision, we believe that continual learning transcends data types and many existing approaches can be applied to natural language processing with few modifications.
